Prior-information-enhanced dynamic magnetic resonance imaging

ABSTRACT

Successive magnetic resonance images are reconstructed from the respective sets of magnetic resonance signals of the dynamic series on the basis of the identified distribution of likelihood of changes and optionally the static reference image. The magnetic resonance signals are acquired by way of a receiver antennae system having a spatial sensitivity profile and in an undersampled fashion and the successive magnetic resonance images are reconstructed optionally also on the basis of the spatial sensitivity profile.

This application is an international 371 filing of PCT/IB03/01929 thatwas filed May 9, 2003, and claims priority therefrom. Additionally, thisapplication also claims priority from EUROPEAN PATENT OFFICE (EPO)application 02076843.8 filed May 13, 2002.

BACKGROUND

The invention pertains to magnetic resonance imaging method to producesuccessive magnetic resonance images.

Such a magnetic resonance imaging method is known from the paper‘Unifying linear prior-information-driven methods for accelerated imageacquisition’ by J. Tsao et al. in MRM46(2001)652–660.

The known magnetic resonance imaging method concerns an acquisition andreconstruction strategy which aims at faster image acquisition. Theknown magnetic resonance imaging method is familiar in the technicalfield of magnetic resonance imaging as Broad-use Linear AcquisitionSpeed-up Technique (BLAST). In the known method a static reference imageis reconstructed from a training set of magnetic resonance signals,

-   -   a distribution of likelihood of changes in the successive        magnetic resonance images is identified from the static        reference image,    -   a dynamic series of sets of magnetic resonance signals is        acquired and    -   the successive magnetic resonance images are reconstructed from        the respective sets of magnetic resonance signals of the dynamic        series on the basis of the identified distribution of likelihood        of changes, and the static reference image.

According to the known method sampling of the dynamic series of setsmagnetic resonance signals is reduced to speed up acquisition. Thesampling of magnetic resonance signals is made quite effective byconstraining the reconstruction to regions in which changes are likely.The successive magnetic resonance image are reconstructed on the basisof the static reference image together with the later acquired dynamicseries of sets of magnetic resonance signals; this dynamic seriesadequately take into account changes that have occurred after theacquisition of the training set of magnetic resonance signals which ledto the static reference image.

Although the known method successfully reduces the signal acquisitiontime, it has several known limitations. Firstly, it requires theacquisition of a static reference image, when the object exhibits littleor no motion. This may not be possible for applications where continuousmotion is involved, such as cardiac imaging. Secondly, the known methodassumes that the spatial distribution of likelihood of changes is known,but it does not describe a technique for estimating it. Therefore, theknown method is restricted to applications where such spatialdistribution can be obtained by other means. Thus, it has appeared thatthere is an ongoing need to shorten the signal acquisition time in orderto better handle rapid and continuous object motion and to furtherreduce image artifacts.

An object of the invention is to provide a magnetic resonance imagingmethod which requires the same or an even shorter signal acquisitiontime relative to the known method, but without the associatedrestrictions while achieving reduced image artifacts and consequentlyimproved image quality.

This object is achieved according to the invention wherein successivesets of magnetic resonance signals are acquired by successively scanningrespective sets of points in k-space such that

-   -   the successive scanning builds up sampling of k-space at    -   the successive scanning covers more frequently a predetermined        portion of k-space at full sampling density and    -   successive magnetic resonance images are reconstructed from the        successive sets of magnetic resonance signals.

According to the invention, full sampling of k-space is built up fromthe successive sets of magnetic resonance signals, where individual setsof magnetic resonance signals at each instant in time may beundersampled. Accordingly, sampling is built up in time and even fullsampling can be achieved as more and more successive sets of magneticresonance signals are acquired. Further, a predetermined portion ofk-space is repeatedly revisited to achieve full sampling of thepredetermined portion earlier than the full sampling of k-space as awhole. This fully sampled predetermined portion of k-space is employedas a training dataset on the basis of which aliasing artefacts caused bythe undersampling in the individual sets of magnetic resonance signals.Preferably, the predetermined portion of k-space that is repeatedlyrevisited concerns a central region of k-space, such as one ore severalbands in the k_(y)-k_(z) plane located around k_(z)=0 or k_(y)=0.

The invention relies on the insight that magnetic resonance signals aregenerally concentrated in the central portion of k-space. Hence, bysuccessive sampling of different positions in the central portion ofk-space at different instants, the distribution of likelihood of changescan be identified from the training data. This distribution isidentified in the space spanned by geometrical space alone or bygeometrical space and temporal frequency.

The invention further relies on the insight that by definition, thestatic reference image does not change over time. Hence, by successivesampling of different positions of k-space at different instants,sampling at full sampling density of k-space is obtained, thus yieldinga fully sampled image, which can be used optionally to obtain a staticreference image. Then, for the peripheral zones of k-space, or for theentire k-space if the training data are acquired in a separate scan,only a sub-sampled set of magnetic resonance signals may be acquired.This reduces the time required for scanning the periphery of k-space, orin a pre-set available time, the periphery of k-space can be scannedoutwardly to a larger extent. Any aliasing or fold-over involved in themagnetic resonance signals from the sub-sampled portion of k-space islifted on the basis of the identified distribution of likelihood ofchanges and optionally the static reference image. Full sampling in thisrespect indicates a sampling density at wavenumber steps less than thereciprocal ‘field-of-view’. Sub-sampling involves a sampling of k-spaceat at sampling density less than full sampling density.

In a preferred implementation of magnetic resonance imaging method ofthe_invention is arranged to produce successive magnetic resonanceimages wherein

-   -   two of successive magnetic resonance signals are acquired in        separate scans or in the same scan by successively scanning        respective sets of points in k-space such that        -   the first set successively scans the central portion or            other portions of k-space where the magnetic resonance            signals are known to be concentrated to yield successive            training data        -   the second set successively scans respective sets of points            in k-space in an undersampled fashion to yield a dynamic            series of successive undersampled data    -   a static reference image is optionally formed from the training        set of magnetic resonance signals,    -   a distribution of likelihood of changes in the successive        magnetic resonance images is identified from the static        reference image and/or the training data, in the space spanned        by geometrical space alone or by geometrical space and temporal        frequency, and    -   the successive magnetic resonance images are reconstructed from        the respective sets of magnetic resonance signals of the dynamic        series on the basis of the identified distribution of likelihood        of changes and if available, the static reference image The        likelihood of change is updated from from time point to time        point, so that method of the invention takes temporal changes in        the likelihood of changes into account.

In a preferred implementation the central portion of k-space issuccessively sampled at a higher sampling than the peripheral region ofk-space, for example at the full sampling density. The acquired data canthen be separated into two sets of magnetic resonance signals forreconstruction: training data and subsampled data. These two sets ofdata may share some common data points. The training data are used toidentify the distribution of likelihood of changes, while the subsampleddata are used optionally to determine the static reference image. Thestatic reference image is optionally reconstructed from the trainingdata and/or undersampled data, or from data acquired separately duringtime periods with little or no motion.

Then, the successive magnetic resonance images are reconstructed fromthe sub-sampled magnetic resonance signals on the basis of theidentified distribution of likelihood of changes and if available, thestatic reference image. Accordingly, sparse, i.e. sub-sampled, samplingand sampling of the low-resolution of training data from the centreportion of k-space is integrated into a single scan.

In another preferred implementation, the magnetic resonance signals areacquired by way of a receiver antennae system having a spatialsensitivity profile. The antennae system contains a number of signalchannels, which process magnetic resonance signals from respectivereceiver antennae, such as surface coils. Often, the spatial sensitivityprofile only shows very slow temporal variations, or the spatialsensitivity profiles does not change with time. Such slow variations maybe caused by slight movement of surface coils that are employed asreceiver antennae and that are placed on the body of the patient to beexamined. Such slight movement may be caused by respiratory motion ofthe patient to be examined. The spatial sensitivity profile of thereceiver antennae system are derived from the static reference images orany time averaged images constructed from the sub-sampled magneticresonance signals. The temporal averaging for constructing such imagesshould be long enough such that k-space, at least the central portion,has been fully sampled. The successive magnetic resonance images arereconstructed from the sub-sampled magnetic resonance signals also onthe basis of the derived spatial sensitivity of the receiver antennaesystem.

The signal channels process magnetic resonance signals from respectivereceiver antennae, such as surface coils. For each signal channel,successive sets of magnetic resonance signals are acquired andreconstructed separately and independently on the basis of the trainingdata and optionally the static reference image, both obtained from thedata from the same signal channel. This yields a separate series ofsuccessive magnetic resonance images for each signal channel. Thesuccessive magnetic resonance images from the multiple signal channelsare combined without explicit a priori knowledge of the coil sensitivityprofile by calculating the root mean square of the image intensity on avoxel-by-voxel basis.

The present invention is particularly advantageously employed insteady-state free processing imaging (SSFP). Power deposition is notablyhigh at stationary magnetic fields of 3T or more, say 7T. The presentinvention allows substantial reduction in data acquisition, allowing forsubstantially reduced power deposition.

Further, the present invention appears to operate particularlyadvantageously when employed in conjunction with a tagging technique,such as CSPAMM. CSPAMM generates a tagging pattern in a region ofinterest, e.g. in a region that includes the patient's heart. Suchtagging techniques have proven to be very valuable in extending theunderstanding of e.g. cardiac dynamics. CSPAMM tagging can be employedto generate a three-dimensional tagging pattern. Preferably, when thepresent invention is applied with a tagging technique, in the centralregion, one or more central bands in k_(y)-k_(z) space are fullysampled, providing the low resolution training data and the outerregions are sub-sampled along a sheared grid pattern in k-t space. A net2.5 fold reduction in scan time relative to full sampling is achieved.

According to the invention, parallel imaging techniques for the signalacquisition and the reconstruction of the magnetic resonance images areincorporated in the known BLAST method, with extension to k-t space i.e.the space spanned by the wavevectors of the magnetic resonance signals,i.e. k-space and time. The parallel imaging techniques, such as SENSEand SMASH involve receiving the magnetic resonance signals in anundersampled fashion so that the received magnetic resonance signalsinclude superposed contributions from spatial positions that are aninteger number of ‘field-of views’ apart. This superposition is thendecomposed into contributions for separate spatial position on the basisof the distribution of likelihood of changes, and optionally the staticreference image and the spatial sensitivity profile of the system ofreceiver antennae system: Preferably, a set of surface coils is employedas a receiver antennae system.

For the acquisition of the training set of magnetic resonance signalsthe acquisition strategy may be chosen from a very wide variety. Somedegree of undersampling may be used to reduce the acquisition time forthe training set. As the training set is acquired only once, or maybe isrefreshed a few times, relatively little time is gained by undersamplingthe training set. More preferably, the training set is acquired suchthat the static reference image has a high spatial resolution and has avery low number of artefacts. This is notably achieved in that thetraining set is acquired with a high k-space sampling density or whensome degree of undersampling is employed, unfolding of aliasingartefacts is undone on the basis of a very accurately determined spatialsensitivity profile of the receiver antennae system.

The time required for acquisition of the magnetic resonance (MR) signalsis reduced by employing sub-sampling of the MR-signals. Suchsub-sampling involves a reduction in k-space of the number of sampledpoints which can be achieved in various ways. Notably, the MR signalsare picked-up through signal channels pertaining to several receiverantennae, such as receiver coils, preferably surface coils. Acquisitionthrough several signal channels enables parallel acquisition of signalsso as to further reduce the signal acquisition time.

Owing to the sub-sampling, sampled data contain contributions fromseveral positions in the object being imaged. The magnetic resonanceimage is reconstructed from the sub-sampled MR-signals on the basis ofthe the distribution of likelihood of changes, and and optionally thestatic reference image and a sensitivity profile associated with thesignal channels. Notably, the sensitivity profile is for example thespatial sensitivity profile of the receiver antennae, such as receivercoils. Preferably, surface coils are employed as the receiver antennae.The reconstructed magnetic resonance image may be considered as beingcomposed of a large number of spatial harmonic components which areassociated with brightness/contrast variations at respectivewavelengths. The resolution of the magnetic resonance image isdetermined by the smallest wavelength, that is by the highest wavenumber(k-value). The largest wavelength, i.e. the smallest wavenumber,involved, is the field-of-view (FOV) of the magnetic resonance image.The resolution is determined by the ratio of the field-of-view and thenumber of samples.

The sub sampling may be achieved in that respective receiver antennaeacquire MR signals such that their resolution in k-space is coarser thanrequired for the resolution of the magnetic resonance image. Thesmallest wavenumber sampled, i.e. the minimum step-size in k-space, isincreased while the largest wavenumber sampled is maintained. Hence, Theimage resolution remains the same when applying sub-sampling, while theminimum k-space step increases, i.e. the FOV decreases. The sub-samplingmay be achieved by reduction of the sample density in k-space, forinstance by skipping lines in the scanning of k-space so that lines ink-space are scanned which are more widely separated than required forthe resolution of the magnetic resonance image. The sub-sampling may beachieved by reducing the field-of-view while maintaining the largestk-value so that the number of sampled points is accordingly reduced.Owing to the reduced field-of-view sampled data contain contributionsfrom several positions in the object being imaged.

Notably, when receiver coil images are reconstructed from sub-sampledMR-signals from respective receiver coils, such receiver coil imagescontain aliasing artefacts caused by the reduced field-of-view. From thereceiver coil images and the sensitivity profiles the contributions inindividual positions of the receiver coil images from differentpositions in the image are disentangled and the magnetic resonance imageis reconstructed. This MR-imaging method is known as such under theacronym SENSE-method. This SENSE-method is discussed in more detail inthe international application no. WO 99/54746-A1.

Alternatively, the sub-sampled MR-signals may be combined into combinedMR-signals which provide sampling of k-space corresponding to the fullfield-of-view. In particular, according to the so-called SMASH-methodsub-sampled MR-signals approximate low-order spatial harmonics which arecombined according to the sensitivity profiles. The SMASH-method isknown as such from the international application no. WO 98/21600.Sub-sampling may also be carried-out spatially. In that case the spatialresolution of the MR-signals is less than the resolution of the magneticresonance image and MR-signals corresponding to a full resolution of themagnetic resonance image are formed on the basis of the sensitivityprofile. Spatial sub-sampling is in particular achieved in thatMR-signals in separate signal channels, e.g. from individual receivercoils, form a combination of contributions from several portions of theobject. Such portions are for example simultaneously excited slices.Often the MR-signals in each signal channel form linear combinations ofcontributions from several portions, e.g. slices. This linearcombination involves the sensitivity profile associated with the signalchannels, i.e. of the receiver coils. Thus, the MR-signals of therespective signal channels and the MR-signals of respective portions(slices) are related by a sensitivity matrix which represents weights ofthe contribution of several portions of the object in the respectivesignal channels due to the sensitivity profile. By inversion of thesensitivity matrix, MR-signals pertaining to respective portions of theobject are derived. In particular MR-signals from respective slices arederived and magnetic resonance images of these slices are reconstructed.

The invention also relates to a magnetic resonance imaging system. It isan object of the invention to provide a magnetic resonance imagingsystem for carrying out the magnetic resonance imaging methods accordingto the invention. A magnetic resonance imaging system of this kind isdefined in the independent Claim 6. The functions of a magneticresonance imaging system according to the invention are preferablycarried out by means of a suitably programmed computer or(micro)processor or by means of a special purpose processor providedwith integrated electronic or opto-electronic circuits especiallydesigned for the execution of one or more of the magnetic resonanceimaging methods according to the invention.

The invention also relates to a computer program with instructions forexecuting a magnetic resonance imaging method. It is a further object ofthe invention to provide a computer program whereby one or more of themagnetic resonance imaging methods according to the invention can becarried out. A computer program according to the invention is defined inthe independent Claim 7. When such a computer program according to theinvention is loaded into the computer of a magnetic resonance imagingsystem, the magnetic resonance imaging system will be capable ofexecuting one or more magnetic resonance imaging methods according tothe invention. For example, a magnetic resonance imaging systemaccording to the invention is a magnetic resonance imaging system whosecomputer is loaded with a computer program according to the invention.Such a computer program can be stored on a carrier such as a CD-ROM. Thecomputer program is then loaded into the computer by reading thecomputer program from the carrier, for example by means of a CD-ROMplayer, and by storing the computer program in the memory of thecomputer of the magnetic resonance imaging system.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects of the invention will be elucidated withreference to the embodiments described hereinafter and with reference tothe accompanying drawing wherein

FIG. 1. Support in x-f space (grey shape, left). x and f denote spatialco-ordinates and temporal frequency, respectively. Support is convolutedwith a point spread function after k-t space sampling, thus leading topotential aliasing.

FIG. 2. (a) k-t space sampling pattern that is periodic in space andtime and also forms a grid pattern. The dots represent the sampledpositions in k-t space. In this example, every 8^(th) phase-encode lineis acquired at every time frame t (b) The corresponding point spreadfunction in discrete x-f space obtained by inverse Fourier transformingthe k-t sampling pattern. Note that there are only 8 non-zero points inthe point spread function

FIG. 3. reconstruction results

FIG. 4 shows diagrammatically a magnetic resonance imaging system inwhich the invention is used.

DETAILED DESCRIPTION

Fast dynamic imaging requires efficient sampling of k-t space, i.e. thespace spanned by the wavevectors of the magnetic resonance signals, i.e.k-space and time. Existing k-t space imaging methods use (or search for)a suitable sampling pattern that prevents the image signals in thereciprocal x-f space, i.e. the geometrical (real) space and frequency,from aliasing. Thus, the fundamental limit lies in the maximum packingof x-f space signals. In the present invention, space extension of theBLAST method is presented, which allows the aliasing to be partiallyresolved in a weighted least-squares manner. This formulation extends tothe use of multiple coils, such as from parallel imaging, which helps tofurther resolve the aliasing.

Introduction

In dynamic imaging, data are acquired at discrete locations in k-spaceover time. From the properties of Fourier transformation, sampling ink-t space leads to convolution of the image signals in the reciprocalx-f space with a point spread function. If the k-t space samplingpattern is a grid-like pattern (i.e. a lattice), the resulting pointspread function will lead to a periodic replication of the x-f spacesignals (FIG. 1). Thus, if the sampling lattice can be adjusted so thatthe replicates of the x-f space signals do not overlap (i.e. alias)significantly, a nearly error-free reconstruction can be obtained fromthe acquired k-t space data. However, the need to avoid any significantaliasing places, a stringent requirement on the sampling pattern, as itis related to the geometric packing of image signals in x-f space.

If some aliasing is allowed in certain parts of x-f space, furtherefficiency can be gained in the k-t space sampling pattern. Theresulting aliasing can still be partially resolved in a weightedleast-squares fashion, if an estimate of the signals is available. Thisapproach is an extension of the BLAST (Broad-use Linear AcquisitionSpeed-up Technique) method to k-t space, using a reference image asprior information to partially resolve the aliasing. If multiplereceiver coils with different sensitivities are available such as in theSENSE (SENSitivity Encoding) method, the additional data can be used tofurther resolve the aliasing in a multi-coil version of thisformulation.

Methods

In BLAST, the reconstructed image is determined as the solution to thefollowing linear system (data-consistency constraint):FT{ρ({overscore (x)})}({overscore (k)} _(t))=d({overscore (k)}_(t))  [1]where FT{.} denotes Fourier transformation; d({overscore (k)}_(t)) isthe measured data at the 1th k-space position. BLAST determinesρ({overscore (x)}) as the feasible solution of Eq. [1] that minimisesthe following norm:∫∥ρ({overscore (x)})−R _(static)({overscore (x)})∥² /∥R_(dynamic)({overscore (x)})+λ∥² d{overscore (x)}  [2]where R_(static)({overscore (x)}) is the static reference image, showingthe baseline signals; R_(dynamic)({overscore (x)}) is the dynamicreference image, highlighting the probable areas of change frombaseline; λ is a scalar-valued regularise to improve conditioning of thelinear system. In the k-t space formulation, {overscore (x)} denotesspatial co-ordinates and temporal frequency, while {overscore (k)}_(t)denotes k-space position and time. A multi-coil version of Eq. [2] is:FT{ρ({overscore (x)})·S _(j)({overscore (x)})}({overscore (k)} _(t))=d_(j)({overscore (k)} _(t))  [3]where S_(j)({overscore (x)}) and d_(j)({overscore (k)}_(t)) denote thesensitivity map and the data measured from the j^(th) coil respectively.

A version of the proposed method that is easy to implement is to dividethe acquisition into the training and the acquisition phases (althoughother schemes are also possible) (FIG. 2). In the training phase, oneobtains prior information to construct R_(dynamic)({overscore (x)}) bysampling k-space at the full field of view but at a low spatialresolution. Depending on the similarity (e.g. in contrast) between theimages acquired from the training and acquisition phases,R_(dynamic)({overscore (x)}) can be set to several possible choices,including:

-   1. the Fourier reconstructed magnitude of the training data in x-f    space;-   2. a fixed temporal frequency filter in x-f space multiplied by a    blurred thresholded version of the Fourier reconstructed training    data to highlight probable areas of change.

For any choice of R_(dynamic)({overscore (x)}), its temporal frequencyDC (“direct current”) term is set to zero, as the DC term is estimatedseparately below.

In the acquisition phase, k-space is sparsely sampled. A sequentialinterleaved pattern is shown in FIG. 2 for simplicity, but othersampling patterns are possible. R_(static)({overscore (x)}) is set tozero, except for the temporal frequency DC term, which is determinedfrom the temporal average of all data in the acquisition phase. Imagesare reconstructed by least-squares fitting of Eqs. [1] or [3] to thesparsely sampled data (with weighting according to Eq. [2]). If the k-tspace sampling pattern is periodic, the computation simplifiestremendously, in a similar fashion to the simplification for CartesianSENSE or Multiple Region MRI.

Results & Discussion

Simulation results are shown using a previously reconstructed cardiacimage sequence. 40 frames were used in low resolution (16 phase-encodelines) for training, while only 25% of the data in the remaining 160frames were used for reconstruction, simulating a fourfold acceleration.The reconstructions with a single or 6 receiver coils were compared withthe original images. Error values indicate the relativeroot-mean-squared (RMS) reconstruction errors (100%=RMS original signalintensity). The errors were <2% in both cases. As expected, the errorswere lower for the multi-coil case due to the data from additionalcoils.

The results show a promising single-/multi-coil approach for efficientand flexible dynamic imaging. Increased acceleration is afforded byallowing slight overlaps in x-f space, which can be partially resolvedwith the use of prior information, and the slight overlaps result innegligible reconstruction errors. Finally, the k-t space sampling doesnot need to be optimised for each specific case, FIG. 4 showsdiagrammatically a magnetic resonance imaging system in which theinvention is used.

The magnetic resonance imaging system includes a set of main coils 10whereby the steady, uniform magnetic field is generated. The main coilsare constructed, for example in such a manner that they enclose atunnel-shaped examination space. The patient to be examined is slid intothis tunnel-shaped examination space. The magnetic resonance imagingsystem also includes a number of gradient coils 11, 12 whereby magneticfields exhibiting spatial variations, notably in the form of temporarygradients in individual directions, are generated so as to be superposedon the uniform magnetic field. The gradient coils 11, 12 are connectedto a controllable power supply unit 21. The gradient coils 11, 12 areenergized by application of an electric current by means of the powersupply unit 21 under control of a control circuit 20. The strength,direction and duration of the gradients are controlled by control of thepower supply unit.

The magnetic resonance imaging system also includes transmission andreceiving coils 13, 16 for generating the RF excitation pulses undercontrol of the control circuit and via a modulator or transmitter 22 andtransmit/receive circuit 15 and for picking up the magnetic resonancesignals, respectively. The transmission coil 13 is preferablyconstructed as a body coil whereby (a part of) the object to be examinedcan be enclosed. The body coil is usually arranged in the magneticresonance imaging system in such a manner that the patient 30 to beexamined, being arranged in the magnetic resonance imaging system, isenclosed by the body coil 13. The body coil 13 acts as a transmissionaerial for the transmission of the R excitation pulses and R refocusingpulses. Preferably, the body coil 13 involves a spatially uniformintensity distribution of the transmitted R pulses RFS. The receivingcoils 16 are preferably surface coils 16 which are arranged on or nearthe body of the patient 30 to be examined. Such surface coils 16 have ahigh sensitivity for the reception of magnetic resonance signals MSwhich is also spatially inhomogeneous. This means that individualsurface coils 16 are mainly sensitive for magnetic resonance signalsoriginating from separate directions, i.e. from separate parts in spaceof the body of the patient to be examined. The coil sensitivity profilerepresents the spatial sensitivity of the set of surface coils.

The transmission coils, notably surface coils, are connected by thetransmit/receive circuit 15 and an amplifier 23 to a demodulator 24 andthe received magnetic resonance signals (MS) are demodulated by means ofthe demodulator 24. The demodulated magnetic resonance signals (DMS) areapplied to a reconstruction unit. The reconstruction unit reconstructsthe magnetic resonance image from the demodulated magnetic resonancesignals (DMS) and on the basis of the coil sensitivity profile of theset of surface coils. The coil sensitivity profile has been measured inadvance and is stored, for example electronically, in a memory unitwhich is included in the reconstruction unit. The reconstruction unitderives one or more image signals from the demodulated magneticresonance signals (DMS), which image signals represent one or more,possibly successive magnetic resonance images. This means that thesignal levels of the image signal of such a magnetic resonance imagerepresent the brightness values of the relevant magnetic resonanceimage.

A reconstruction unit 25 in practice is preferably constructed as adigital image processing unit 25 which is programmed so as toreconstruct the magnetic resonance image from the demodulated magneticresonance signals and on the basis of the coil sensitivity profile. Thedigital image processing unit 25 is notably programmed so as to executethe reconstruction in conformity with the so-called SENSE technique orthe so-called SMASH technique. The image signal from the reconstructionunit is applied to a monitor 26 so that the monitor can display theimage information of the magnetic resonance image (images). It is alsopossible to store the image signal in a buffer unit 27 while awaitingfurther processing, for example printing in the form of a hard copy.

In order to form a magnetic resonance image or a series of successivemagnetic resonance images of the patient to be examined, the body of thepatient is exposed to the magnetic field prevailing in the examinationspace. The steady, uniform magnetic field, i.e. the main field, orientsa small excess number of the spins in the body of the patient to beexamined in the direction of the main field. This generates a (small)net macroscopic magnetization in the body. These spins are, for examplenuclear spins such as of the hydrogen nuclei (protons), but electronspins may also be concerned. The magnetization is locally influenced byapplication of the gradient fields. For example, the gradient coils 12apply a selection gradient in order to select a more or less thin sliceof the body. Subsequently, the transmission coils apply the RFexcitation pulse to the examination space in which the part to be imagedof the patient to be examined is situated. The RF excitation pulseexcites the spins in the selected slice, i.e. the net magnetization thenperforms a precessional motion about the direction of the main field.During this operation those spins are excited which have a Larmorfrequency within the frequency band of the RF excitation pulse in themain field. However, it is also very well possible to excite the spinsin a part of the body which is much larger than such a thin slice; forexample, the spins can be excited in a three-dimensional part whichextends substantially in three directions in the body. After the RFexcitation, the spins slowly return to their initial state and themacroscopic magnetization returns to its (thermal) state of equilibrium.The relaxing spins then emit magnetic resonance signals. Because of theapplication of a read-out gradient and a phase encoding gradient, themagnetic resonance signals have a plurality of frequency componentswhich encode the spatial positions in, for example the selected slice.The k space is scanned by the magnetic resonance signals by applicationof the read-out gradients and the phase encoding gradients. According tothe invention, the application of notably the phase encoding gradientsresults in the sub-sampling of the k space, relative to a predeterminedspatial resolution of the magnetic resonance image. For example, anumber of lines which is too small for the predetermined resolution ofthe magnetic resonance image, for example only half the number of lines,is scanned in the although sampling optimisation can be used to furtherimprove the reconstruction.

The invention has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be constructed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

1. A magnetic resonance imaging method to produce successive magnetic resonance images of a region of a static subject, the magnetic resonance imaging method comprising: acquiring temporally successive sets of magnetic resonance signals from the region of the subject, wherein the region contains a moving subregion of the subject, by successively scanning the region to produce respective sparsely sampled sets of data points in k-space such that (i) the successive scanning builds up a sampling density of k-space and (ii) the successive scanning of the region covers more frequently a predetermined portion of k-space such that the predetermined portion is fully sampled; determining a static MR image directly from the acquired temporally successive sets of magnetic resonance signals; and reconstructing temporally successive magnetic resonance images from a combination of the successive sets of magnetic resonance signals, the static MR image, and a spatial distribution of a likelihood of changes in the successively reconstructed magnetic resonance images.
 2. A magnetic resonance imaging method as claimed in claim 1, wherein the successive scanning generates: successive updates of a training set of magnetic resonance signals formed from the magnetic resonance signals, which successively scanned the predetermined portion of k-space, successive updates of an undersampled set of magnetic resonance signals formed from the magnetic resonance signals, which successively scanned the entire k-space in an undersampled fashion, and the distribution of likelihood of changes in the successively reconstructed magnetic resonance images is identified from at least one of the static MR image and the training data, either (1) in the space spanned by geometrical space alone or by the combination of the geometrical space and temporal frequency.
 3. A magnetic resonance imaging method as claimed in claim 2, wherein the reconstructed magnetic resonance signals are acquired by way of a receiver antennae system having a spatial sensitivity profile and in that the spatial sensitivity profile is derived from at least one of the training data or the undersampled data after time averaging, or from separately acquired data; the successive magnetic resonance images are reconstructed also on the basis of the derived spatial sensitivity profile.
 4. A magnetic resonance imaging method as claimed in claim 2, wherein the reconstructed magnetic resonance signals are acquired by way of a receiver antennae system having a spatial sensitivity profile the reconstructed magnetic resonance signals are acquired along a number of signal channels, the successive magnetic resonance images are reconstructed separately and independently for each signal channel, and successive magnetic resonance images from the multiple signal channels are combined by calculating a root mean square of signal intensity on a voxel-by-voxel basis, without knowledge of the spatial sensitivity profile.
 5. A magnetic resonance imaging system arranged in order to acquire a training set of magnetic resonance signals, acquire a dynamic series of sets of magnetic resonance signals in an undersampled fashion with a receiver antenna system having a spatial sensitivity profile; identify a distribution of a likelihood of changes in the successively reconstructed magnetic resonance images from the training data, in the space spanned by either: geometrical space alone or the combination of the geometrical space and temporal frequency; and reconstruct the successively reconstructed magnetic resonance images directly from the respective sets of magnetic resonance signals of the dynamic series on the basis of the identified distribution of the likelihood of changes in a static image and the spatial sensitivity profile.
 6. A computer readable medium comprising instructions for controlling a computer system configured for performing a magnetic resonance imaging method comprising steps of: acquiring a set of training data of magnetic resonance signals; acquiring a dynamic series of sets of magnetic resonance data with a spatial sensitivity profile in an undersampled fashion; reconstructing a static image directly from at least one of the set of training data, the undersampled magnetic resonance data, and data acquired separately during time periods with little or no motion; identifying a distribution of a likelihood of changes in the successively acquired sets of magnetic resonance data directly from at least one of the static image and the training data, in a space spanned by geometrical space alone or by either: a combination of the geometrical space and temporal frequency; reconstructing the successively acquired sets of magnetic resonance data into a dynamic series of successively reconstructed magnetic resonance images on the basis of the identified distribution of the likelihood of changes the static image, and the spatial sensitivity profile.
 7. A magnetic resonance imaging method to produce successive magnetic resonance images, the magnetic resonance imaging method comprising: acquiring temporally successive sets of magnetic resonance signals including at least a plurality of undersampled acquisition sets of magnetic resonance signals that are undersampled in k-t space; determining a static image from the acquired temporally successive set of magnetic resonance signals directly; determining a spatial distribution of a likelihood of changes from the acquired temporally successive sets of magnetic resonance signals directly; and reconstructing the plurality of undersampled acquisition sets of magnetic resonance signals using the determined static image and the determined spatial distribution of the likelihood of changes to produce a temporally successive set of magnetic resonance images.
 8. The magnetic resonance imaging method as claimed in claim 7, wherein the determining of the static image includes: temporally combining data of the plurality of undersampled acquisition sets of magnetic resonance signals on a weighted basis to generate the static image.
 9. The magnetic resonance imaging method as claimed in claim 7, wherein the acquiring of temporally successive sets magnetic resonance signals further includes: acquiring training data at a field of view of the reconstructed magnetic resonance images and at a spatial resolution that is lower than a spatial resolution of the reconstructed magnetic resonance images, the spatial distribution of likelihood of changes being determined from the training data.
 10. The magnetic resonance imaging method as claimed in claim 9, wherein the determining of the spatial distribution of likelihood of changes includes at least one of: generating the spatial distribution of likelihood of changes as a Fourier reconstructed magnitude of the training data in x-f space, and generating the spatial distribution of likelihood of changes as a fixed temporal frequency filter in x-f space multiplied by a blurred thresholded version of the Fourier reconstructed training data.
 11. The magnetic resonance imaging method as claimed in claim 7, wherein the acquiring of temporally successive sets of magnetic resonance signals further includes: concurrently, acquiring the temporally successive sets of magnetic resonance signals using a plurality of receiver antennae.
 12. The magnetic resonance imaging method as claimed in claim 7, wherein the reconstructing step determines a feasible solution, ρ({overscore (x)}) which minimizes: ∫∥ρ({overscore (x)})−R _(static)({overscore (x)})∥² /∥R _(dynamic)({overscore (x)})+λ∥² d{overscore (x)} where R_(static)({overscore (x)}) is the static image, showing the baseline signals; R_(dynamic)({overscore (x)}) is a reference image showing the determined spatial distribution of changes, and λ is a scalar which improves linearity.
 13. The magnetic resonance imaging method as claimed in claim 12, wherein the sets of magnetic resonance signals are acquired in parallel with coils wit different sensitivity profiles and the reconstruction is determined as the solution of: FT{ρ({overscore (x)})·S _(j)({overscore (x)})}({overscore (k)} _(t))=d _(j)({overscore (k)} _(t)) where FT{ } denotes a Fourier transformation, {overscore (x)}denotes spatial co-ordinates and temporal frequency, {overscore (k)}_(t) denotes k-space position and time, S_(j)({overscore (x)}) and d_(j)({overscore (k)}_(t)) denote a sensitivity map of and a portion of the magnetic resonance signals that are received by a j^(th) of the coils. 